Bibliographic record
Abstract
Abstract This article situates current deep learning (DL) artificial intelligence (AI) within Leroi-Gourhan’s deep history of the human species’ relation to technology. According to Leroi-Gourhan, technology is both a key element of anthropogenesis and a source of later tensions (or disentanglement) between the human species and its external and increasingly autonomous technics. Human organic (life-oriented) intelligence at first extends itself through technical (machine-oriented) intelligence, only to be later left behind by it. We propose a concept of machine intelligence that goes beyond technical intelligence, the latter a (still) hybrid human–machine intelligence. This new, emerging machine intelligence is DL AI. DL AI developed out of the failure of symbolic AI to instantiate a key generic component of intelligence: creativity. While symbolic AI was rigid and pre-programmed, DL is flexible and unpredictable, presenting an embryonic form of actual machine intelligence. Its creativity can be likened to the ancient Greek concept of metis, a cunning and polymorphous form of intelligence. Although often biased and problematic, DL exhibits a machine creativity that goes beyond the anthropocentric imaginings of AI as a (mechanistic) imitation of the human norm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".